1. science
2. mathematics
3. statistics

CHAPTER 3
Data Description
Objectives
 Summarize data using measures of central tendency, such as the mean, median,
mode, and midrange.
 Describe data using the measures of variation, such as the range, variance, and
standard deviation.
 Identify the position of a data value in a data set using various measures of position,
such as percentiles, deciles, and quartiles.
 Use the techniques of exploratory data analysis, including boxplots and five-number
summaries to discover various aspects of data.
Introduction
Measures of average are called measures of central tendency and include the mean,
median, mode, and midrange.
Measures that determine the spread of the data values are called measures of
variation and include the range, variance, and standard deviation.
Measures of a specific data value’s relative position in comparison with other data
values are called measures of position and include percentiles, deciles, and quartiles.
Section 3-1 Measures of Central Tendency
 A statistic is a characteristic or measure obtained by using the data values from a
sample.
 A parameter is a characteristic or measure obtained by using all the data values for a
specific population.
I.
Mean and Mode
The symbol for a population mean is .
The symbol for a sample mean is x (read “x bar”).
The mean is the sum of the values, divided by the total number of values.
 sample mean 
 population mean 
x is any data value from the data set
n is the total number of data (n is called the sample size)
Rounding Rule for the Mean: The mean should be rounded to one more decimal place
than occurs in the raw data.
Example 1: Find the mean of 24, 28, 36
Mode is the value that occurs most often in a data set. A data set can have more
than one mode or no mode at all.
Example 2: Find the mode of 2.3 2.4 2.8 2.3 4.5 3.1.
Example 3: Find the mode of 3 4 7 8 11 13
The mode is the only measure of central tendency that can be used in finding the
most typical case when the data are categorical.
The procedure for finding the mean for grouped data uses the midpoints of the
 f  xm .
classes. The formula for finding the mean of grouped data is x 
n
The modal class is the class with the largest frequency.
Example 4:
Thirty automobiles were tested for fuel efficiency (in miles per gallon). Find the
mean fuel efficiency and the modal class for the frequency distribution obtained from the
thirty automobiles.
Class boundaries
Frequency
7.5 – 12.5
3
12.5 – 17.5
5
17.5 – 22.5
15
22.5 – 27.5
5
27.5 – 32.5
2
II.
Median and Midrange
The median is the midpoint in a data set.
The symbol for a sample median is MD
1. Reorder the data from small to large
2. Find the data that represents the middle position
Example 1: Find the median
(a) 35, 48, 62, 32, 47
(b) 25.4, 26.8, 27.3, 27.5, 28.1, 26.4
Example 2: Find the median
3, 5, 32, 6, 13, 11, 8, 19, 21, 6
Midrange is the sum of the lowest and highest values in a data set, divided by 2.
Example 3: Find the midrange of 17, 16, 15, 13, 17, 12, 10.
Example 4: The average undergraduate grade point average (GPA) for the top 9
ranked-medical schools are listed below.
3.80 3.86 3.83 3.78 3.75 3.75 3.86 3.70 3.74
Find (a) the mean, (b) the median, (c) the mode, and (d) the midrange.
III.
Weighted Mean
Weighted Mean – multiply each value by its corresponding weight and dividing the sum
of the products by the sum of the weights.
x
x
w1 x1  w2 x2  w3 x3  ......  wn xn
w1  w2  w3  .......  wn
 wx
w
where w1 , w2 ,........, wn are the weights and x1 , x 2 ,......., xn are the values.
Example 1:
An instructor gives four 1-hour exams and one final exam, which counts as two 1-hour
exams. Find the student’s overall average if she received 83, 65, 70, and 72 on the 1hour exams and 78 on the final exam.
Example 2: Grade distributions for a Math 227 class: In class-8%; tests-52%; computer
exam-10%; and final exam-30%. A student had grades of 82, 75, 94, and 78
respectively, In class, tests, computer exam, and final exam. Find the student’s final
average.
Properties of the Mean (pg 124)
 Uses all data values.
 Varies less than the median or mode
 Used in computing other statistics, such as the variance
 Unique, usually not one of the data values
 Cannot be used with open-ended classes
 Affected by extremely high or low values, called outliers
Properties of the Median (pg 124)
 Gives the midpoint
 Used when it is necessary to find out whether the data values fall into the upper
half or lower half of the distribution.
 Can be used for an open-ended distribution.
 Affected less than the mean by extremely high or extremely low values.
Properties of the Mode (pg 124)
 Used when the most typical case is desired
 Easiest average to compute
 Can be used with nominal data
 Not always unique or may not exist
Properties of the Midrange (pg 124)
 Easy to compute.
 Gives the midpoint.
 Affected by extremely high or low values in a data set
Types of Distributions Figure 3-1
Section 3-2 Measures of Variation
I.
Range, sample variance, and sample standard deviation
Range is the highest value minus the lowest value.
R = highest value – lowest value
Example 1: Find the range of 32, 78, 54, 65, 89.
Text: Example 3-18/19: Outdoor Paint
The measures of variance and standard deviation are used to determine the
consistency of a variable.
Variance is the average of the square of the distance that each value is from the mean.
Measure the dispersion away from the mean
e.g. 5, 8, 11
What is the mean?
Logically  sum up differences then divide it by 3
To avoid the cancellation, take the squared deviations.
Sum of the squares =
Average of the sum of the squares =
(Variance)
Standard deviation
(Take the square root of the variance) =
Formulas for calculating variance and standard deviation
Definition Formulas
Variance of a sample
Standard Deviation of a sample
s2 
 ( x  x)
s
 ( x  x)
2
n 1
2
n 1
s  variance
Computational Formulas
Variance of a sample
s
2
x

2
 ( x)2 / n 
(n  1)
Example 2: Use the definition formula to find the variance and standard deviation of
5, 8, 11
x
x=
x x
( x  x) 2
 (x
 x) 2 =
Sample variance:
Sample standard deviation:
Example 3: Use the definition formula to find the standard deviation of
5.8, 4.6, 5.3, 3.8, 6.0
x=
x
x x
( x  x) 2
 (x
Sample variance:
Sample standard deviation:
 x) 2
Example 4: Use the computational formula to find the standard deviation of
5.8, 4.6, 5.3, 3.8, 6.0
x2
x
Note: Both the mean and standard deviation are sensitive to extreme observations
called the outliers. The standard deviation is used to describe variability
when the mean is used as a measure of central tendency.
II.
Variance and standard deviation for grouped data
The formula is similar to the computational formula of s 2 for a data set
s2 
 f x
2
m
2
   f  xm  / n 


n 1
Example 1:
These data represent the net worth (in millions of dollars) of 50 businesses in a large
city. Find the variance and standard deviation.
Class Limits
10 -20
Frequency
5
21 – 31
10
32 – 42
3
43 – 53
7
54 – 64
18
65 – 75
7
III.
Coefficient of variation
The coefficient of variation is a measure of relative variability that expresses standard
deviation as a percentage of the mean.
CVar 
s
100%
x
When comparing the standard deviations of two different variables, the coefficient of
variations are used.
Example 1:
The average score on an English final examination was 85, with a standard deviation of
5; the average score on a history final exam was 110, with a standard deviation of 8.
Compare the variations of the two.
IV.
Range Rule of Thumb
The range can be used to approximate the standard deviation. This approximation is
called the range rule of thumb.
The Range Rule of Thumb:
Example: Using the range rule of thumb, approximate the standard deviation for the
data set 5, 8, 8, 9, 10, 12, and 13.
Note: The range rule of thumb is only an approximation and should be used when the
distribution of data is unimodal and roughly symmetric.
Measures of Variation: Range Rule of Thumb
Use
to approximate the lowest value and
value in a data set.
to approximate the highest
Example: X  10, Range  12
V.
Chebyshev’s Theorem and Empirical Rule
Chebyshev’s Theorem (Any distribution shape)
The proportion of values from a data set that will fall with k standard deviation of the
mean will be at least 1 – 1/k2, where k is a number greater than 1 .
Empirical Rule ( A bell-shaped distribution)
Approximately 68% of the data values will fall within 1 standard deviation of the mean.
Approximately 95% of the data values will fall within 2 standard deviations of the mean.
Approximately 99.7% of the data values will fall within 3 standard deviations of the
mean.
Example 1:
The average U.S. yearly per capita consumption of citrus fruit is 26.8 pounds. Suppose
that the distribution of fruit amounts consumed is bell-shaped with a standard deviation
of equal to 4.2 pounds. What percentage of Americans would you expect to consume in
the range of 18.4 pounds to 35.2 pounds of citrus fruit per year?
Example 2:
Using Chebyshev’s theorem, solve these problems for a distribution with a mean of 50
and a standard deviation of 5. At least what percentage of the values will fall between
40 and 60?
Example 3:
A sample of the labor costs per hour to assemble a certain product has a mean of $2.60
and a standard deviation of $0.15. Using Chebyshev’s theorem, find the range in which
at least 88.89% of the data will lie.
Section 3-3 Measures of Position
I.
z score
A z score represents the number of standard deviations that a data value lies above or
below the mean.
z
xx
s
Example 1:
Which of these exam grades has a better relative position?
(a) A grade of 56 on a test with x = 48 and s = 5.
(b) A grade of 220 on a test with x = 200 and s = 10.
Example 2:
Human body temperatures have a mean of 98.20 and a standard deviation of 0.62. An
Emergency room patient is found to have a temperature of 101. Convert 101 to a z
score. Consider a data to be extremely unusual if its z score is less than -3.00 or greater
than 3.00. Is that temperature unusually high? What does it suggest?
II.
Percentiles and Quartiles
Percentile Formula (Percentile Rank)
The percentile corresponding to a given value x is computed by using the following
formula:
(number of values below x)  0.5
Percentile 
100%
total number of values
Example 1:
Find the percentile rank for each test score in the data set.
5, 15, 21, 16, 20, 12
Formula for finding a value corresponding to a given percentile ( Pm )
Pm - is the number that separates the bottom m % of the data from the top
(100- m )% of the data.
e.g. If your test score represented 90th percentile means that 90% of the
people who took the test scored lower than you and only 10% scored
higher than you.
Finding the location of Pm :
m
Evaluate (
)n
100
m
m
1. If (
) n is a whole number, then location of Pm is [ (
) n +0.5].
100
100
The percentile of Pm is halfway between the data value in position
m
(
) n and the data value in the next position.
100
m
2. If (
) n is not a whole number, then location of Pm is the next higher
100
whole number.
The percentile of Pm is the data value in this location.
Quartiles are defined as follows:
The first Quartile Q1 = P25
The second Quartile Q2 = P50
The third Quartile Q3 = P75
Example 2: The number of home runs hit by American League home run
leaders in the year 1959 – 1998. These ordered data are
22 32 32 32 32 33 36 36 37 39 39 39 40 40
40 40 41 42 42 43 43 44 44 44 44 45 45 46
46 48 49 49 49 49 50 51 52 56 56 61
Find the following:
(a) P77
(b) P42
(c) Q1
(d) Q3
III. The Interquartile Range
The interquartile range, or IQR
IQR = Q3 - Q1
The interquartile range is not influenced by extreme observations.
If the median is used as a measure of central tendency, then the
interquartile range should be used to describe variability.
Identifying Outliers (extremely high or low data value)
Any data value that is smaller than Q1- 1.5 x IQR or larger than
Q3 + 1.5x IQR
is considered as an outlier.
The quick way to find Q1 and Q3:
Find the median of the data values that fall below Q2 is Q1.
Find the median of the data values that fall above Q2 is Q3.
Example 1: Consider the following ranked data:
.09 .14 .25 .37 .55 .55 .56
.77 .86 .93 1.15 1.34 1.41 1.75
3.69 3.90 4.50 4.88 7.79
(a) Find the interquartile range
(b) Is 7.79 an outlier?
.60 .77
2.01 2.23
Example 2: Check the following data set for outliers.
145 119 122 118 125 100
Section 3-4 Exploratory Data Analysis
I.
Boxplot
The median and the interquartile range are used to describe the distribution
using a graph called a boxplot. From a boxplot, we can detect any skewness in
the shape of the distribution and identify any outliers in the data set.
Constructing a boxplot
Find the 5-number summary consisting of the Low, Q1, Q2 (median), Q3, and High.
Construct a scale with values that include the Low and High.
Construct a box with two vertical sides called the hinges above Q1 and Q3 on the
axis.
Also construct a vertical line in the box above Q2.
Finally, connect the Low and High to the hinges using horizontal lines called
the whiskers.
Example 1:
Construct a boxplot for the number of calculators sold during a randomly
selected week.
8, 12, 23, 5, 9, 15, 3
Example 2:
The following ranked data represent the number of English-language
Sunday newspaper in each of the 50 states.
2 3 3
4 4
4
4
4
5
6
6
6
7
7 7 8 10 11 11 11 12 12 13 14 14 14
15 15 16 16 16 16 16 16 18 18 19 21 21
23 27 31 35 37 38 39 40 44 62 85
Construct a boxplot for the data.
Example 3:
For the boxplot given below, (a) identify the maximum value, minimum value,
first quartile, median, third quartile, and interquartile range; (b) comment on the
shape of the distribution; (c) identify a suspected outlier.
----------------------I +
I--------------------*
----------------------------+---------+---------+---------+---------+-------48
60
72
84
96
II.
The distribution shape
A bell-shaped distribution
0.3
14
13
Density
C2
0.2
12
0.1
11
10
0.0
10
11
12
C2
13
14
A slightly skewed to the right distribution
14
0.3
13
Density
C3
0.2
12
0.1
11
0.0
10
10
11
12
13
14
C2
A slightly skewed to the left distribution
0.3
15
14
C1
12
Density
0.2
13
0.1
11
10
0.0
10
11
12
13
14
15
C2
Summary
 Some basic ways to summarize data include measures of central tendency,
measures of variation or dispersion, and measures of position.
 The three most commonly used measures of central tendency are the mean, median,
and mode. The midrange is also used to represent an average.
 The three most commonly used measurements of variation are the range, variance,
and standard deviation.
 The most common measures of position are percentiles, quartiles, and deciles.
 Data values are distributed according to Chebyshev’s theorem and in special cases,
the empirical rule.